PyTorch implementation of a version of the Deep Embedded Clustering (DEC) algorithm. Compatible with PyTorch 1.0.0 and Python 3.6 or 3.7 with or without CUDA.

This follows (or attempts to; note this implementation is unofficial) the algorithm described in "Unsupervised Deep Embedding for Clustering Analysis" of Junyuan Xie, Ross Girshick, Ali Farhadi (https://arxiv.org/abs/1511.06335).

An example using MNIST data can be found in the examples/mnist/mnist.py which achieves around 85% accuracy.

Here is an example confusion matrix, true labels on y-axis and predicted labels on the x-axis.

This is distributed as a Python package ptdec and can be installed with python setup.py install after installing ptsdae from https://github.com/vlukiyanov/pt-sdae. The PyTorch nn.Module class representing the DEC is DEC in ptdec.dec, while the train function from ptdec.model is used to train DEC.

• Original Caffe: https://github.com/piiswrong/dec
• PyTorch: https://github.com/CharlesNord/DEC-pytorch and https://github.com/eelxpeng/dec-pytorch
• Keras: https://github.com/XifengGuo/DEC-keras and https://github.com/fferroni/DEC-Keras
• MXNet: https://github.com/apache/incubator-mxnet/blob/master/example/deep-embedded-clustering/dec.py
• Chainer: https://github.com/ymym3412/DeepEmbeddedClustering